A 3D millifluidic model of a dermal perivascular microenvironment on a chip.

The process of angiogenesis plays a pivotal role in skin regeneration, ensuring the provision of nutrients and oxygen to the nascent tissue, thanks to the formation of novel microvascular networks supporting functional tissue regeneration. Unfortunately, most of the current therapeutic approaches for skin regeneration lack vascularization, required to promote effective angiogenesis. Thus, tridimensional models, complemented with specific biochemical signals, can be a valuable tool to unravel the neovascularization mechanisms and develop novel clinical strategies.

Detection and localization of Goliath grouper using their low-frequency pulse sounds.

The goal of this paper is to implement and deploy an automated detector and localization model to locate underwater marine organisms using their low-frequency pulse sounds. This model is based on time difference of arrival (TDOA) and uses a two-stage approach, first, to identify the sound and, second, to localize it. In the first stage, an adaptive matched filter (MF) is designed and implemented to detect and determine the timing of the sound pulses recorded by the hydrophones.

Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data.

This study addresses the challenge represented by the application of deep learning models to the prediction of ocean dynamics using datasets over a large region or with high spatial or temporal resolution In a previous study by the authors of this article, they showed that such a challenge could be met by using a divide and conquer approach. The domain was in fact split into multiple sub-regions, which were small enough to be predicted individually and in parallel with each other by a deep learning model. At each time step of the prediction process, the sub-model solutions would be merged at the boundary of each sub-region to remove discontinuities between consecutive domains in order to predict the evolution of the full domain.

Biophysical larval dispersal models of observed bonefish (Albula vulpes) spawning events in Abaco, The Bahamas: An assessment of population connectivity and ocean dynamics.

Biophysical models are a powerful tool for assessing population connectivity of marine organisms that broadcast spawn. Albula vulpes is a species of bonefish that is an economically and culturally important sportfish found throughout the Caribbean and that exhibits genetic connectivity among geographically distant populations. We created ontogenetically relevant biophysical models for bonefish larval dispersal based upon multiple observed spawning events in Abaco, The Bahamas in 2013, 2018, and 2019.

Physics-Informed Tensor-Train ConvLSTM for Volumetric Velocity Forecasting of the Loop Current.

According to the National Academies, a week long forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies at a given location is a critical step toward understanding their effects on the gulf ecosystems as well as toward anticipating and mitigating the outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM). However, creating such a forecast has remained a challenging problem since LC behavior is dominated by dynamic processes across multiple time and spatial scales not resolved at once by conventional numerical models. In this paper, building on the foundation of spatiotemporal predictive learning in video prediction, we develop a physics informed deep learning based prediction model called-Physics-informed Tensor-train ConvLSTM (PITT-ConvLSTM)-for forecasting 3D geo-spatiotemporal sequences.